SOTAVerified

Inference Attack

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 283 papers

TitleStatusHype
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks—0
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information—0
A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis—0
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models—0
A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models—0
AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation—0
AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models—0
A Volumetric Approach to Privacy of Dynamical Systems—0
A Zero Auxiliary Knowledge Membership Inference Attack on Aggregate Location Data—0
A Blessing of Dimensionality in Membership Inference through Regularization—0
Black-Box Training Data Identification in GANs via Detector Networks—0
BLIA: Detect model memorization in binary classification model through passive Label Inference attack—0
Bounding Information Leakage in Machine Learning—0
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning—0
Similarity-based Label Inference Attack against Training and Inference of Split Learning—0
Inferring Communities of Interest in Collaborative Learning-based Recommender Systems—0
Comprehensive Privacy Analysis on Federated Recommender System against Attribute Inference Attacks—0
Curvature Clues: Decoding Deep Learning Privacy with Input Loss Curvature—0
DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks—0
Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models—0
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges—0
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning—0
Defending Model Inversion and Membership Inference Attacks via Prediction Purification—0
De-identification is not always enough—0
DeSIA: Attribute Inference Attacks Against Limited Fixed Aggregate Statistics—0
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation—0
Differentially Private Data Generative Models—0
Differentially Private k-Means Clustering with Guaranteed Convergence—0
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks—0
Disparate Privacy Vulnerability: Targeted Attribute Inference Attacks and Defenses—0
Do Backdoors Assist Membership Inference Attacks?—0
Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?—0
DP-GPL: Differentially Private Graph Prompt Learning—0
Eavesdrop the Composition Proportion of Training Labels in Federated Learning—0
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers—0
τ: Gradient-based and Task-Agnostic machine Unlearning—0
On the Effectiveness of Regularization Against Membership Inference Attacks—0
On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel—0
On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models—0
On the Privacy Risk of In-context Learning—0
Orthogonal Soft Pruning for Efficient Class Unlearning—0
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers—0
Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation—0
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding—0
PPA: Preference Profiling Attack Against Federated Learning—0
Preserving Privacy in GANs Against Membership Inference Attack—0
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning—0
Privacy Against Inference Attacks in Vertical Federated Learning—0
Privacy Attacks Against Biometric Models with Fewer Samples: Incorporating the Output of Multiple Models—0
Privacy-Preserving Collaborative Learning through Feature Extraction—0
Show:102550
← PrevPage 3 of 6Next →

No leaderboard results yet.